Extended Kalman filter for fouling detection in thermal power plant reheater

Summary This paper deals with the design and application of nonlinear model‐based control schemes for stable and nonlinear benchmark industrial processes. The primary control objective is to facilitate set‐point (constant/time‐varying) tracking in the presence of external disturbances, process noise, measurement noise, parametric uncertainty, and model mismatch. We first propose a “noninferential‐type” model‐based control scheme which involves a finite‐dimensional, nonlinear, and deterministic process model to generate the model states. Secondly, an “inferential‐type” model‐based control scheme has been introduced particularly to take into account the stochastic uncertainties such as process noise and measurement noise. The second scheme exploits the dual extended Kalman filter for estimating the immeasurable states and the process parameters through which disturbance is injected. Unlike fixed‐parameter controllers, the proposed schemes update the controller gains at each step depending on the real‐time process gains. In order to demonstrate the usefulness of the proposed closed‐loop tracking control schemes, two exhaustive case studies have been carried out on the CSTR and Van de Vusse reactor processes, which are considered to be benchmark industrial processes due to highly nonlinear and unpredictable behaviour and due to nonminimum phase property. Finally, the performance of the proposed schemes are compared with an EKF‐based adaptive PI control framework and the simulation results reveal that the transient performance of the proposed schemes are better than that of the aforementioned PI technique especially in perturbed condition (ie, in presence of model mismatch and measurement noise).

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“…The outlines of DEKF algorithm 40,42,55 are given in the following. A nonlinear process can be described as…”

Section: Estimation Of Controller Parameters Using Ekfmentioning

confidence: 99%

“…Apart from that, joint EKF (JEKF) and dual EKF (DEKF) are used for joint or sequential estimation of both states and parameters . Sivathanu and Subramanian have proposed a model‐based online foul monitoring approach for a thermal power plant reheater in which JEKF and DEKF are used simultaneous estimation of states and plant parameters.…”

Section: Introductionmentioning

confidence: 99%

Design and application of nonlinear model‐based tracking control schemes employing DEKF estimation

Bhadra

Panda

Bhowmick

et al. 2019

Optim Control Appl Methods

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“…If the soot blowing operation is not carried out in time, it will cause too much ash deposition on the heating surface, reduce the heat transfer efficiency, and even cause accidents, which will affect the safety and economy of boiler production; if the soot blowing frequency is too high, it will not only cause excessive waste of soot blowing steam, but also cause erosion of heating surface and affect the service life of equipment. Therefore, under the limited conditions of soot blowing cost, production efficiency, and production constraints, it is of great significance to determine the most efficient soot blowing operation mode for the safety of the whole unit, energy saving and emission reduction [6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

Prediction of Pollution State of Heating Surface in Coal-Fired Utility Boilers

Yan

Liu

et al. 2020

IEEE Access

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“…Digital signal processing tools are also widely used in the data processing and analysis of boiler monitoring to optimize the ash fouling monitoring system. A dual-extended Kalman filter has been used to estimate the influencing factors of ash deposition, and a cleaning factor indicator has been applied to reflect the fouling degree of heating surfaces [18]. Sobota introduced a method for determining the heat-flow parameters of a steam boiler [19] that can perform online calculations of heating flow rates absorbed by boiler furnaces and superheaters.…”

Section: Introductionmentioning

confidence: 99%

Online Ash Fouling Prediction for Boiler Heating Surfaces based on Wavelet Analysis and Support Vector Regression

et al. 2019

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Depending on its operating conditions, traditional soot blowing is activated for a fixed time. However, low-frequency soot blowing can cause heat transfer efficiency to decrease. High-frequency soot blowing not only wastes high-pressure steam, but also abrades surface pipes, reducing the working life of a heat exchange device. Therefore, it is necessary to design an online ash fouling monitoring system to perform soot blowing that is dependent on the status of ash accumulation. This study presents an online monitoring model of ash-layer thermal resistance that reflects the degree of ash fouling. A wavelet threshold denoising algorithm was applied to smooth the thermal resistance of the ash layer calculated by the heat balance mechanism model. Thus, the variation in thermal resistance becomes more visible, which is more conducive to optimizing the operation of soot blowing. The designed Support Vector Regression (SVR) model could achieve the online prediction of thermal resistance denoising for low-temperature superheaters. Experimental analysis indicates that the prediction accuracy was 98.5% during the testing phase. By using the method proposed in this study, online monitoring of heating surfaces during the ash fouling process can be realized without adding complicated and expensive equipment.

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Extended Kalman filter for fouling detection in thermal power plant reheater

Cited by 27 publications

References 16 publications

Design and application of nonlinear model‐based tracking control schemes employing DEKF estimation

Design and application of nonlinear model‐based tracking control schemes employing DEKF estimation

Prediction of Pollution State of Heating Surface in Coal-Fired Utility Boilers

Online Ash Fouling Prediction for Boiler Heating Surfaces based on Wavelet Analysis and Support Vector Regression

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